By Topic

Self-lifting scheme: new approach for generating and factoring wavelet filter bank

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $33
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
X. X. Chen ; The National Engineering Research Center for T & D, China Electric Power Research Institute, No. 15 Xiaoying Donglu, Qinghe, Beijing 100085, People¿s Republic of China E-mail: ; Y. Y. Chen

The authors presents a new lifting scheme, the self-lifting scheme, and prove that self-lifted wavelets based on orthogonal or biorthogonal wavelets remain biorthogonal. In contrast to self-lifting, the existing lifting scheme can be called cross-lifting. Compared with cross-lifting, the self-lifting scheme provides new approaches for constructing biorthogonal wavelets, as well as factorising wavelet filter bank (WFB). For constructing wavelets, the self-lifting-based method updates one part of a wavelet filter by the other part of the same filter and obtains two updated filters in one pass, whereas the cross-lifting based method updates one filter by another filter and obtains one updated filter in one pass. To factorise WFB, self-lifting takes one part of a filter as the factor to decompose the other part of the same filter and obtains two factorised filters in one pass, whereas cross-lifting based one takes one part of a filter as the factor to decompose the corresponding part of the other filter and obtains one factorised filter in one pass. Several examples show how to use self-lifting scheme to produce new wavelets with desirable properties, how to factorise complex WFBs into simple lifting filter banks, how to implement self-lifting-based discrete wavelet transform (WT) in z-domain and in time domain and why lifting-based WT is superior to convolution-based one.

Published in:

IET Signal Processing  (Volume:2 ,  Issue: 4 )